Server, analysis method and computer program product for analyzing recognition information and combination information

ABSTRACT

According to an embodiment, a server includes a first acquiring unit, a second acquiring unit, an analyzing unit, and an output unit. The first acquiring unit is configured to acquire recognition information includes a product identification information for identifying the product. The second acquiring unit is configured to acquire combination information including the product identification information of the product to be combined with an object image including an object. The analyzing unit is configured to calculate product priorities for respective products by analyzing the recognition information and the combination information. The output unit is configured to output information based on the product priorities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a division of application Ser. No. 14/065,670, filed Oct. 29,2013, which is based upon and claims the benefit of priority fromJapanese Patent Application No. 2012-243762, filed on Nov. 5, 2012; theentire contents of both applications are incorporated herein byreference.

FIELD

Embodiments described herein relate generally to a server, an analysismethod, and a computer program product.

BACKGROUND

In retail industry for general consumers, there are recently increasingattempts to differentiate shopping styles by creating new userqualities, and O2O (Online to Offline), for example, is attractingattention. The O2O means interaction of online and offline buyingbehaviors and influence of online information on buying behavior atbrick-and-motor shops or the like, and services such as finding storesusing location-based services of portable terminals and couponsavailable online and usable at brick-and-motor shops have beenexpanding.

In the meantime, various technologies relating to O2O such as technologyfor virtual fitting using product images are being developed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a system according to afirst embodiment;

FIG. 2 is a diagram illustrating an example of a first terminalaccording to the first embodiment;

FIG. 3 is a diagram illustrating an example of a second terminalaccording to the first embodiment;

FIG. 4 is a diagram illustrating an example of a server according to thefirst embodiment;

FIG. 5 is a table illustrating examples of recognition informationaccording to the first embodiment;

FIG. 6 is a table illustrating examples of combination informationaccording to the first embodiment;

FIG. 7 is a diagram illustrating an example of a data structure ofproduct information according to the first embodiment;

FIG. 8 is a flowchart illustrating an example of processing according tothe first embodiment;

FIG. 9 is a diagram illustrating an example of a system according to asecond embodiment;

FIG. 10 is a diagram illustrating an example of a third terminalaccording to the second embodiment;

FIG. 11 is a diagram illustrating an example of a server according tothe second embodiment;

FIG. 12 is a table illustrating examples of purchase informationaccording to the second embodiment;

FIG. 13 is a table illustrating examples of first sales promotioninformation before being updated according to the second embodiment;

FIG. 14 is a table illustrating examples of first sales promotioninformation after being updated according to the second embodiment;

FIG. 15 is a table illustrating examples of store layout informationbefore being updated according to the second embodiment;

FIG. 16 is a table illustrating examples of store layout informationafter being updated according to the second embodiment;

FIG. 17 is a flowchart illustrating an example of processing accordingto the second embodiment;

FIG. 18 is a diagram illustrating an example of a system according to amodification; and

FIG. 19 is a diagram illustrating an example of a hardware configurationof the server according to the embodiments and modifications.

DETAILED DESCRIPTION

According to an embodiment, a server includes a first acquiring unit, arecognition information storage unit, a second acquiring unit, acombination information storage unit, an analyzing unit, and an outputunit. The first acquiring unit is configured to acquire a piece ofrecognition information including a piece of product identificationinformation for identifying a product included in a product image. Therecognition information storage unit is configured to store the piece ofrecognition information. The second acquiring unit is configured toacquire a piece of combination information including the piece ofproduct identification information of the product to be combined with anobject image including an object. The combination information storageunit is configured to store the piece of combination information. Theanalyzing unit is configured to calculate product priorities forrespective products by analyzing a plurality of pieces of recognitioninformation stored in the recognition information storage unit and aplurality of pieces of combination information stored in the combinationinformation storage unit. The output unit is configured to outputinformation based on the product priorities.

Embodiments will be described in detail below with reference to theaccompanying drawings.

First Embodiment

FIG. 1 is a configuration diagram illustrating an example of a system 1according to a first embodiment. As illustrated in FIG. 1, the system 1includes a first terminal 10, a second terminal 20, and a server 30. Thefirst terminal 10, the second terminal 20 and the server 30 areconnected via a network 2. The network 2 can be realized by the Internetor a local area network (LAN), for example.

In the first embodiment, an example in which the first terminal 10 is animage recognition terminal that includes a recognizing unit 11 and thatacquires related information on a real object of interest to the user bybeing held over the real object will be described. The first terminal 10can be realized by a portable terminal, for example. In the following,to acquire related information on a real object by focus the firstterminal 10 over the real object may be referred to as “focus”.

Similarly, in the first embodiment, an example in which the secondterminal 20 is an image combining terminal that includes a combiningunit 21 and that performs virtual fitting simulation, virtualinstallation simulation and the like will be described. The secondterminal 20 is installed in a store selling products, for example. Inthe following, to experience a product of interest to the user throughvirtual fitting simulation, virtual installation simulation or the likemay be referred to as “try”.

With the system 1, it is assumed that the user holds the first terminal10 over a real object of interest to acquire related information on theproduct and that, starting from the acquired related information, theuser is encouraged to go to the store in which the second terminal 20 isinstalled and to experience the product through virtual fittingsimulation or virtual installation simulation, which is linked topurchase of the product.

FIG. 2 is a configuration diagram illustrating an example of the firstterminal 10 according to the first embodiment. As illustrated in FIG. 2,the first terminal 10 includes a recognizing unit 11, an imaging unit12, a feedback information storage unit 13, a display unit 14, and anoutput unit 15.

The recognizing unit 11 may be implemented by making a processor such asa central processing unit (CPU) execute a program, that is, by software,may be implemented by hardware such as an integrated circuit (IC), ormay be implemented by combination of software and hardware, for example.The imaging unit 12 can be realized by an imager such as a digitalcamera, for example. The feedback information storage unit 13 can berealized by a storage device that can magnetically, optically orelectrically store information such as a hard disk drive (HDD), a solidstate drive (SSD), a memory card, an optical disk, or a random accessmemory (RAM), for example. The display unit 14 can be realized by adisplay device such as a liquid crystal display or a touch paneldisplay, for example. The output unit 15 can be realized by acommunication device such as a network interface card (NIC), forexample.

The imaging unit 12 images a real object of interest to the user togenerate a product image. Examples of the real object of interest to theuser include an advertisement of a product of interest to the user, butthe real object may be a product itself of interest to the user.

The feedback information storage unit 13 stores feedback information.Details of the feedback information will be described later.

The recognizing unit 11 includes an image recognizing unit 16 and afeedback unit 17.

The image recognizing unit 16 recognizes a product image, estimates aproduct included in the product image, and selects at least any one of aplurality of kinds of related information on the product. Specifically,the image recognizing unit 16 acquires product information on theestimated, product from the server 30 and selects at least any one of aplurality of kinds of related information contained in the acquired,product information. The product information acquired by the imagerecognizing unit 16 contains a product ID (an example of productidentification information) of the estimated product and a plurality ofkinds of related information. Examples of the kinds of relatedinformation include attribute information and accompanying informationof the estimated product. Examples of the attribute information includebrand, price, color, and material, and examples of the accompanyinginformation include word of mouth, recommended coordinates and storeinformation (address, map, etc.).

When feedback information of the estimated product is stored in thefeedback information storage unit 13, the image recognizing unit 16selects related information according to the feedback information.

The feedback unit 17 stores feedback information based on informationtransmitted from the server 30 in the feedback information storage unit13.

The display unit 14 displays the related information selected by theimage recognizing unit 16. The display unit 14 displays word of mouth,recommended coordinates, store information or the like of the productestimated by the image recognizing unit 16 as an image, for example.

The output unit 15 outputs the recognition information to the server 30.The recognition information at least contains a product ID of theproduct estimated by the image recognizing unit 16. The recognitioninformation may contain product image information and relatedinformation of the product image. The product image information may bethe product image itself or may be an image matched with the productimage in image recognition performed by the image recognizing unit 16 oran image ID of the image. The recognition information may contain thedate and time of recognition, the position of recognition, a user ID ofthe user, and the like.

FIG. 3 is a configuration diagram illustrating an example of the secondterminal 20 according to the first embodiment. As illustrated in FIG. 3,the second terminal 20 includes a combining unit 21, an imaging unit 22,a feedback information storage unit 23, a display unit 24, and an outputunit 25.

The combining unit 21 may be implemented by making a processor such as acentral processing unit (CPU) execute a program, that is, by software,may be implemented by hardware such as an IC, or may be implemented bycombination of software and hardware, for example. The imaging unit 22can be realized by an imager such as a digital camera, for example. Thefeedback information storage unit 23 can be realized by a storage devicethat can magnetically, optically or electrically store information suchas an HDD, an SSD, a memory card, an optical disk, or a RAM, forexample. The display unit 24 can be realized by a display device such asa liquid crystal display or a touch panel display, for example. Theoutput unit 25 can be realized by a communication device such as an NIC,for example.

The imaging unit 22 images an object to be combined to generate an imageto be combined. Examples of the object to be combined include the user.

The feedback information storage unit 23 stores feedback information.Details of the feedback information will be described later.

The combining unit 21 includes an image combining unit 26 and a feedbackunit 27.

The image combining unit 26 combines the image to be combined generatedby the imaging unit 22 and an image for combination of a product (suchas clothes). Specifically, the image combining unit 26 acquires productinformation of a plurality of products from the server 30, displaysimages for combination contained in the acquired product information onthe display unit 24, and combines an image for combination selected bythe user with the image to be combined generated by the imaging unit 22.The product information acquired by the image combining unit 26 containsproduct IDs (an example of product identification information) of theproducts and a group of images for combination. Since the images forcombination are present for each category of products, the images forcombination are in a form of groups. The category may be the kind or theuse of products or the state in which products are tried on.

When feedback information is stored in the feedback information storageunit 23, the image combining unit 26 displays the images for combinationon the display unit 24 in a manner that the user can preferentiallyselect an image for combination indicated by the feedback information.

The feedback unit 27 stores feedback information based on informationtransmitted from the server 30 in the feedback information storage unit23.

The display unit 24 displays images for combination to be selected bythe user and combined images obtained by combination by the imagecombining unit 26.

The output unit 25 outputs combination information to the server 30. Thecombination information at least contains a product ID of the productestimated by the image combining unit 26. The combination informationmay contain combined image information of an image to be combined andcombination image information of an image for combination. The combinedimage information may be the image to be combined itself or may containdepth information obtained by sensing the image to be combined, skeletoninformation indicating the outline of a person, and measurementinformation such as height, weight, chest circumference, sitting heightand the like in addition to the image to be combined. The combinationimage information may be the image for combination itself or may be animage ID of the image for combination. The combination information maycontain the date and time of combination, the position of combination, auser ID of the user, the category of the product and the like.

FIG. 4 is a configuration diagram illustrating an example of the server30 according to the first embodiment. As illustrated in FIG. 4, theserver 30 includes a first acquiring unit 31, a recognition informationstorage unit 32, a second acquiring unit 33, a combination informationstorage unit 34, an analyzing unit 35, an output unit 36, and a productinformation storage unit 37.

The first acquiring unit 31, the second acquiring unit 33 and theanalyzing unit 35 may be implemented by making a processor such as acentral processing unit (CPU) execute a program, that is, by software,may be implemented by hardware such as an integrated circuit (IC), ormay be implemented by combination of software and hardware, for example.The recognition information storage unit 32, the combination informationstorage unit 34, and the product information storage unit 37 can berealized by a storage device that, can magnetically, optically orelectrically store information such as an HDD, an SSD, a memory card, anoptical disk, or a RAH, for example. The output unit 36 can be realizedby a communication device such as an NIC, for example.

The first acquiring unit 31 acquires recognition information includingat least the product ID of the product estimated by the recognizing unit11 from the recognizing unit 11 (the output unit 15), and stores theacquired recognition information in the recognition information storageunit 32. The recognition information may further contain, information asmentioned in the description of the output unit 15.

The recognition information storage unit 32 stores a plurality of piecesof recognition information stored by the first acquiring unit 31. FIG. 5is a table illustrating examples of the recognition informationaccording to the first embodiment. In the examples illustrated in FIG.5, the recognition information is information in which a number, thedate and time of recognition, the product image information, a productID, and the related information displayed at the first terminal 10 areassociated, but the recognition. information is not limited thereto.

The second acquiring unit 33 acquires combination information includingat least the product ID of the product in the image for combinationcombined with the image to be combined from, the combining unit 21 (theoutput unit 25), and stores the acquired combination information in thecombination information storage unit 34. The combination information mayfurther contain information as mentioned in the description of theoutput unit 25.

The combination information storage unit 34 stores a plurality of piecesof combination information stored by the second acquiring unit 33. FIG.6 is a table illustrating examples of the combination informationaccording to the first embodiment. In the examples illustrated in FIG.6, the combination information is information in which the number, thedate and time of combination, the combined image information, theproduct ID and the category are associated, but the combinationinformation is not limited thereto.

The analyzing unit 35 analyzes a plurality of pieces of recognitioninformation stored in the recognition information storage unit 32 and aplurality of pieces of combination information stored in the combinationinformation storage unit 34, and calculates product priority of eachproduct. Specifically, the analyzing unit 35 analyzes the pieces ofrecognition information to calculate first product priority of each,product, analyzes the pieces of combination information to calculatesecond product priority of each product, and calculate the productpriority of each product on the basis of the first product priority andthe second product priority of each product.

For example, the analyzing unit 35 calculates the product priority E ofa certain product by calculating the first product priority Er and thesecond product priority Es of the product and calculating weightedaddition of the calculated first product priority Er and second productpriority Es as expressed by Equation (1). if the product ID of theproduct for which the first product priority Er is calculated is notpresent in the pieces of combination information, the second productpriority Es of the product is obviously 0, and if the product ID of theproduct for which the second priority Es is calculated is not present inthe pieces of recognition information, the first product priority Er ofthe product is obviously 0.E=wr×Er+ws×Es  (1)

In Equation (1), wr represents the weight of the priority Er and wsrepresents the weight of the priority Es. Note that the analyzing unit35 analyzes the pieces of recognition information and sets the firstproduct priority Er of the product represented by a product IDassociated with recognition date and time to be higher as therecognition date and time is closer to the current date and time. Thatis, the analyzing unit 35 sets the first product priority Er to behigher for a product over which a terminal was held on the date and timecloser to the current date and time.

The analyzing unit 35 also analyzes the pieces of recognitioninformation and sets the first product priority Er to be higher for aproduct represented by a product ID having a value whose number ofoccurrences is larger. That is, the analyzing unit 35 sets the firstproduct priority Er to be higher for a product over which a terminal washeld a larger number of times.

Similarly, the analyzing unit 35 analyzes the pieces of combinationinformation and sets the second product priority Es of a productrepresented by a product ID associated with combination date and timecontained in the combination information to be higher as the combinationdate and time is closer to the current date and time. That is, theanalyzing unit 35 sets the second product priority Es to be higher for aproduct that was tried on the date and time closer to the current dateand time.

The analyzing unit 35 also analyzes the pieces of combinationinformation and sets the second product priority Es to be higher for aproduct represented by a product. ID having a value whose number ofoccurrences is larger. That is, the analyzing unit 35 sets the secondproduct priority Es to be higher for a product that was tried a largernumber of times.

The analyzing unit 35 further analyzes whether or not combinationinformation including a product ID of a product whose product priority Esatisfies a first predetermined condition exists in the pieces ofcombination information, and generates first recommendation informationrecommending related information according to the analysis result amonga plurality of kinds of related information. The first predeterminedcondition may be a threshold or may be the product priorities E from thehighest priority to a certain predetermined rank of priority.

For example, if combination information including a product ID of aproduct whose product priority E satisfies the first predeterminedcondition does not exist in the pieces of combination information, theanalyzing unit 35 generates first recommendation informationrecommending store information among a plurality of kinds of relatedinformation. In this case, since “focus” is performed but “try” is notperformed, it is possible to encourage the user to perform “try” byrecommending the store information of the store in which the secondterminal 20 is installed, and as a result, it may be possible tomotivate the user to buy the product.

If, for example, combination information including a product ID of aproduct whose product priority E satisfies the first predeterminedcondition exists in the pieces of combination information, the analyzingunit 35 generates first recommendation information recommendingrecommended coordinates among a plurality of kinds of relatedinformation. In this case, since both “focus” and “try” are performed,it may be possible to motivate the user to buy other productsrecommended in the recommended coordinates by recommending therecommended coordinates.

Furthermore, if there exists a plurality of categories of a productwhose product priority satisfies a second predetermined condition, theanalyzing unit 35 analyzes the number of occurrences of each of thecategories in a plurality of pieces of combination information andgenerates second recommendation information recommending a category withthe largest number of occurrences. The second predetermined conditionmay be a threshold or may be the product priorities E from the highestpriority to a certain predetermined rank of priority.

For example, it is assumed that a product with the product priority Esatisfying the second predetermined condition is a bag that can becarried in three ways: a handbag, a shoulder bag, and a backpack. Inthis case, since the categories of the bag are handbag, shoulder bag andbackpack, the analyzing unit 35 analyzes the number of occurrence ofeach of handbag, shoulder bag and backpack in the pieces of combinationinformation. Then, if the number of occurrences of shoulder bag is thelargest, the analyzing unit 35 generates second recommendationinformation recommending the shoulder bag. In this case, since it ispopular among users to perform “try” on the shoulder bag, it may bepossible to motivate the user to buy the product by recommending theshoulder bag. If, however, “try” on the shoulder bag style of the bag isalready performed, second recommendation information recommendinganother category such as handbag or backpack on which “try” has not beenperformed may be generated.

The output unit 36 outputs information regarding a product based on theproduct priority calculated by the analyzing unit 35 to at least one ofthe recognizing unit 11 and the combining unit 21. The information basedon the product priority may be the product priority itself or may berelated information or an image for combination of the product with theproduct priority. The related information and the image for combinationcan be obtained from the product information storage unit 37.

The output unit 36 also outputs information based on the productpriority calculated by the analyzing unit 35 and the firstrecommendation information generated by the analyzing unit 35 to therecognizing unit 11. The information based on the product priority andthe first recommendation information may be information indicating theproduct priority and recommended related information or may berecommended related information on the product with the productpriority.

The output unit 36 also outputs information based on the productpriority calculated by the analyzing unit 35 and the secondrecommendation information generated by the analyzing unit 35 to thecombining unit 21. The information based on the product priority and thesecond recommendation information may be the product priority and animage ID of a recommended image for combination or may be a recommendedimage for combination of the product with the product priority.

The information output by the output unit 36 in this manner is used asfeedback information at the recognizing unit 11 and the combining unit21, so that information with higher probability of motivating the userto buy a product is preferentially displayed at the first terminal 10and the second terminal 20.

When it is requested by the image recognizing unit 16 to acquire productinformation, the output unit 36 acquires the requested productinformation from the product information storage unit 37 and outputs theacquired product information to the image recognizing unit 16.Similarly, when it is requested by the image combining unit 26 toacquire product information, the output unit 36 acquires the requestedproduct information from the product information storage unit 37 andoutputs the acquired product information to the image combining unit 26.

The product information storage unit 37 stores product information ofproducts. FIG. 7 is a diagram illustrating an example of a datastructure of the product information according to the first embodiment.In the example illustrated in FIG. 7, the product information isinformation in which a product ID, attribute information (brand, price,color, material, etc.), accompanying information (word of mouth,recommended coordinates, store information (address, map, etc.), etc.)and a group of images for combination are associated, but the productinformation is not limited thereto.

FIG. 8 is a flowchart illustrating an example of a flow of procedures ofprocessing performed by the server 30 according to the first embodiment.

First, the first acquiring unit 31 acquires recognition informationincluding at least a product ID of a product estimated by therecognizing unit 11 from the recognizing unit 11 (the output unit 15),and stores the acquired recognition information in the recognitioninformation storage unit 32 (step S101).

Subsequently, the second acquiring unit 33 acquires combinationinformation including at least a product ID of a product in an image forcombination combined with an image to be combined from the combiningunit 21 (the output unit 25), and stores the acquired combinationinformation in the combination information storage unit 34 (step S103).

Subsequently, the analyzing unit 35 analyzes a plurality of pieces ofrecognition information, stored in the recognition information storageunit 32 to calculate first product priority of each product, analyses aplurality of pieces of combination information stored in the combinationinformation storage unit 34 to calculate second product priority of eachproduct, and calculates product priority of each product on the basis ofthe first product priority and the second product priority of eachproduct (step S105).

Subsequently, the analyzing unit 35 further analyzes whether or notcombination information including a product ID of a product whoseproduct priority satisfies the first predetermined condition exists inthe pieces of combination information, and generates firstrecommendation information recommending related information according tothe analysis result among a plurality of kinds of related information(step S107).

Subsequently, the output unit 36 outputs information based on theproduct priority calculated by the analyzing unit 35 and the firstrecommendation information generated by the analyzing unit 35 to therecognizing unit 11 (step S109). Subsequently, if there exists aplurality of categories of a product whose product priority satisfiesthe second predetermined condition, the analyzing unit 35 analyzes thenumber of occurrences of each of the categories in the pieces ofcombination, information and generates second recommendation informationrecommending a category with the largest number of occurrences (stepS111).

Subsequently, the output unit 36 outputs information based on theproduct priority calculated by the analyzing unit 35 and the secondrecommendation information generated by the analyzing unit 35 to thecombining unit 21 (step S113).

As described above, according to the first embodiment, since the productpriority taking history of various O2O related technologies intoconsideration can be calculated by analyzing the history of therecognition information, and the history of the combination informationto calculate the product priority, products of greater interest to theuser can be extracted. In addition, according to the first embodiment,since information based on the calculated product priority is output tothe recognizing unit and the combining unit, the recognizing unit andthe combining unit can preferentially present products of greaterinterest to the user by using the information and it is thus possible toincrease the probability of motivating the user to buy a product.

In particular, according to the first embodiment, since not onlyinformation on a product of higher interest to the user but alsoinformation with high probability of motivating the user to buy aproduct can be extracted from a plurality of kinds of relatedinformation of the product, the recognizing unit can preferentiallypresent information of greater interest to the user by using theinformation and it is thus possible to increase the probability ofmotivating the user to buy a product.

Similarly, according to the first embodiment, since not only informationon a product of higher interest to the user but also information withhigh probability of motivating the user to buy a product can beextracted from the categories of the product, the combining unit canpreferentially present information of greater interest to the user byusing the information and it is thus possible to increase theprobability of motivating the user to buy a product.

According to the first embodiment, since the recognizing unit 11 (theoutput unit 15) can contain product image information and relatedinformation in the recognition information, it is also possible tofigure out over what real objects the user held the terminal and whatproducts the user is interested in. For example, it is possible tofigure out whether the user got interested in a product X by focus theterminal over an advertisement A or by focus the terminal over anadvertisement B, which allows the history through which the user gotinterested in the product X to be used in the analysis.

Similarly, according to the first embodiment, since the combining unit21 (the output unit 25) can contain, combined image information andcombination image information in the combination information, it is alsopossible to figure out what image for combination is combined with whatimage to be combined. For example, it is possible to figure out such afact that people with a body type A often try on clothes Y or such afact that people with a body type B often try on clothes Z, and it isthus possible to obtain a tendency of “try” of each user by dataanalysis.

Second Embodiment

In the second embodiment, an example in which a third terminal includinga managing unit that manages sales information on sales of products isfurther provided will be described. In the following, the differencefrom the first embodiment will be mainly described and components havingsimilar functions as in the first embodiment will be designated by thesame names and reference numerals as in the first embodiment, and thedescription thereof will not be repeated.

FIG. 9 is a configuration diagram illustrating an example of a system101 according to the second embodiment. As illustrated in FIG. 9, thesystem 101 is different from that in the first embodiment in a server130 and a third terminal 140 thereof.

In the second embodiment, an example in which, the third terminal 140 isa management terminal that includes a managing unit 141 and that managessales information related to sales of products will be described.

FIG. 10 is a configuration diagram illustrating an example of the thirdterminal 140 according to the second embodiment. As illustrated in FIG.10, the third terminal 140 includes a managing unit 141, a salesinformation storage unit 142, a display unit 143, and an output unit144.

The managing unit 141 may be implemented by making a processor such as aCPU execute a program, that is, by software, may be implemented byhardware such as an IC, or may be implemented by combination of softwareand hardware, for example. The sales information storage unit 142 can berealized by a storage device that can magnetically, optically orelectrically store information such as an HDD, an SSD, a memory card, anoptical disk, or a RAH, for example. The display unit 143 can berealized by a display device such as a liquid crystal display or a touchpanel display, for example. The output unit 144 can be realized by acommunication device such as an NIC, for example.

The sales information storage unit 142 stores sales information relatedto sales of products. Examples of the sales information include purchaseinformation indicating details of purchase of a product, sales promotioninformation relating to sales promotion of a product, customerinformation, inventory information, and training information relating totraining of store staff. The purchase information contains at least aproduct ID of a product to be purchased. The purchase information mayalso contain the date and time of purchase. The sales promotioninformation contains first sales promotion information relating to salespromotion using product images and second sales promotion informationrelating to sales promotion using images for combination. Examples ofthe sales promotion information include information on advertisingstrategy, store layout, procurement plan, product lineup, and methodsfor recommending products to customers.

The managing unit 141 manages the sales information stored in the salesinformation storage unit 142.

The display unit 143 displays the sales information managed by themanaging unit 141.

The output unit 144 outputs the sales information to the server 130. Forexample, the output unit 144 outputs purchase information and salespromotion information to the server 130.

FIG. 11 is a configuration diagram illustrating an example of the server130 according to the second embodiment. As illustrated in FIG. 11, theserver 130 is different from that in the first embodiment in ananalyzing unit 135, a third acquiring unit 138, and a sales informationstorage unit 139.

The third acquiring unit 138 may be implemented by making a processorsuch as a CPU execute a program, that is, by software, may beimplemented by hardware such as an IC, or may be implemented bycombination of software and hardware, for example. The sales informationstorage unit 139 can be realized by a storage device that canmagnetically, optically or electrically store information such as anHDD, an SSD, a memory card, an optical disk, or a RAM, for example.

The third acquiring unit 133 acquires purchase information and salespromotion information including at least a product ID of a product to bepurchased from the managing unit 141 (the output unit 144), and storesthe acquired purchase information and sales promotion information in thesales information storage unit 139. Mote that the purchase informationand the sales promotion information may further contain informationmentioned in the description of the sales information storage unit 142.

The sales information storage unit 139 stores a plurality of pieces ofpurchase information and sales promotion information stored by the thirdacquiring unit 138. FIG. 12 is a table illustrating examples of thepurchase information according to the second embodiment. In the examplesillustrated in FIG. 12, the purchase information is information in whicha number, the date and time of purchase, and a product ID areassociated, but the purchase information is not limited thereto.

The analyzing unit 135 performs at least one of first analysis ofanalyzing a plurality of pieces of recognition information stored in therecognition information storage unit 32, a plurality of pieces ofcombination information stored in the combination information storageunit 34, and a plurality of pieces of purchase information stored in thesales information storage unit 139 to calculate the product priority ofeach product and a second analysis of analyzing at least either aplurality of pieces of recognition information or a plurality of piecesof combination information in addition to a plurality of pieces ofpurchase information to obtain updated contents of sales information.

First, the first analysis will be described.

The analyzing unit 135 analyzes a plurality of pieces of recognitioninformation to calculate first product priority of each product,analyzes a plurality of pieces of combination information to calculatesecond product priority of each product, analyzes a plurality of piecesof purchase information to calculate third product priority of eachproduct, calculate the product priority of each product on the basis ofthe first product priority, the second product priority and the thirdproduct priority of each product. For example, the analyzing unit 135calculates the product priority E of a certain product by calculatingthe first product priority Er, the second product priority Es and thethird product priority Eb of the product and calculating weightedaddition of the calculated first product priority Er, second productpriority Es and third product priority Eb as expressed by Equation (2).E=wr×Er+ws×Es+wb×Eb  (2)

In Equation (2), wb represents the weight of the priority Eb.

Note that the analyzing unit 135 analyzes the pieces of recognitioninformation and sets the first product priority Er of the productrepresented by a product ID associated with recognition date and time tobe higher as the recognition date and time is closer to the current dateand time. That is, the analyzing unit 135 sets the first productpriority Er to be higher for a product over which a terminal was held onthe date and time closer to the current date and time.

The analyzing unit 135 also analyzes the pieces of recognitioninformation and sets the first product priority Er to be higher for aproduct represented by a product ID having a value whose number ofoccurrences is larger. That is, the analyzing unit 135 sets the firstproduct priority Er to be higher for a product over which a terminal washeld a larger number of times.

Similarly, the analyzing unit 135 analyzes the pieces of combinationinformation and sets the second product priority Es of a productrepresented by a product ID associated with combination date and timecontained in the combination information to be higher as the combinationdate and time is closer to the current date and time. That is, theanalyzing unit 135 sets the second product priority Es to be higher fora product that was tried on the date and time closer to the current dateand time.

The analyzing unit 135 also analyzes the pieces of combinationinformation and sets the second product priority Es to be higher for aproduct represented by a product ID having a value whose number ofoccurrences is larger. That is, the analyzing unit 135 sets the secondproduct priority Es to be higher for a product that was tried a largernumber of times.

Similarly, the analyzing unit 135 analyzes the pieces of purchaseinformation, and sets the third product priority Eb of a productrepresented by a product ID associated with purchase date and timecontained in the purchase information to be higher as the purchase dateand time is closer to the current date and time. That is, the analyzingunit 135 sets the third product priority Eb to be higher for a productthat, was purchased on the date and time closer to the current date andtime.

The analyzing unit 135 also analyzes the pieces of purchase informationand sets the third product priority Eb to be higher for a productrepresented by a product ID having a value whose number of occurrencesis larger. That is, the analyzing unit 135 sets the third productpriority Eb to be higher for a product that was purchased a largernumber of times.

Since the generation of the first recommendation information and thesecond recommendation information is the same as that in the firstembodiment, the description thereof will not be repeated.

Next, the second analysis will be described.

The analyzing unit 135 determines whether or not the behavior of “focus”and the behavior of “try” of the user led to purchase of a product byanalyzing at least either a plurality of pieces of recognitioninformation or a plurality of pieces of combination information inaddition to a plurality of pieces of purchase information and obtainsupdated contents of sales promotion information.

Specifically, the analyzing unit 135 analyzes a plurality of pieces ofpurchase information, analyzes the number of occurrences, in the piecesof recognition information, of product image information associated witha product ID having a value whose number of occurrences in the purchaseinformation satisfies a third predetermined condition, and obtainsupdated contents of the first sales promotion information according tothe number of occurrences of the product image information. The thirdpredetermined condition may be thresholds in multiple steps including anincrease determination threshold for determining whether or not toincrease a value and a decrease determination threshold for determiningwhether or not to decrease a value, for example.

FIG. 13 is a table illustrating examples of the first sales promotioninformation before being updated according to the second embodiment, andFIG. 14 is a table illustrating examples of the first sales promotioninformation after being updated according to the second embodiment. Inthe examples illustrated in FIGS. 13 and 14, the first sales promotioninformation is information in which a number, an advertisement ID, animage ID (product image information) of a product image, and the numberof advertisements are associated, but the first sales promotioninformation is not limited thereto. In the examples illustrated in FIG.13, it is assumed that the number of occurrences, in the pieces ofpurchase information, of the value of product ID associated with each ofimage IDs “IMAGE 10392” and “IMAGE 10192” satisfies the increasedetermination threshold while the number of occurrences, in the piecesof purchase information, of the value of product ID associated withimage ID “IMAGE 10291” satisfies the decrease threshold. It is alsoassumed that the numbers of occurrences, in the pieces of recognitioninformation, of the image IDs “IMAGE 10392” and “IMAGE 10192” are largerthan an average while the number of occurrences of the image ID “IMAGE10291” is much smaller than the average.

That is, it is found that focus over an advertisement A and anadvertisement C led to purchase of products for the productscorresponding to the image IDs “IMAGE 10392” and “IMAGE 10192”, theeffect of the advertisement A and the advertisement C is high, and salespromotion using the advertisement A and the advertisement C is thereforeto be enhanced. On the other hand, it is found that focus over anadvertisement B had not led to purchase of the product for the productcorresponding to the image ID “IMAGE 10291”, the effect of theadvertisement B is low, and sales promotion using the advertisement B isto be reduced.

In this case, the analyzing unit 135 obtains updated contents in whichthe numbers of advertisement for the image IDs “IMAGE 10392” and “IMAGE10192” are increased by 10 while the number of advertisements for theimage ID “IMAGE 10291” is decreased by 20, for example, as the updatedcontents of the first sales promotion information. As a result, it ispossible to update the first sales promotion information illustrated inFIG. 13 with that as illustrated in FIG. 14.

The analyzing unit 135 also analyzes the pieces of purchase information,analyzes the number of occurrences, in the pieces of combinationinformation, of combination image information associated with a productID having a value whose number of occurrences in the purchaseinformation satisfies a fourth predetermined condition, and obtainsupdated contents of the second sales promotion information according tothe number of occurrences of the combination image information. Thefourth predetermined condition may be thresholds in multiple stepsincluding an increase determination threshold for determining whether ornot to increase a value and a decrease determination threshold fordetermining whether or not to decrease a value, for example.

The analyzing unit 135 can also obtain updated contents of store layoutby analyzing a plurality of pieces of purchase information anddetermining the sales rate of products sold together. The sales rate ofproducts sold together can be calculated from purchase date and time orthe like in the purchase information.

FIG. 15 is a table illustrating examples of the store layout informationbefore being updated according to the second embodiment, and FIG. 16 isa table illustrating examples of the store layout information afterbeing updated according to the second embodiment. In the examplesillustrated in FIGS. 15 and 16, the store layout information isinformation in which a number, a shelf ID, and a product ID areassociated, but the store layout information is not limited thereto. Itis assumed that a shelf A and a shelf B are adjacent to each other whilea shelf C is adjacent to neither of the shelf A and the shelf B. In theexamples illustrated in FIG. 15, it is assumed that the sales rates ofproducts sold together of products IDs “PRODUCT 20928” and “PRODUCT20290” satisfy the increase determination threshold.

That is, since the rate of being sold together is high for the productIDs “PRODUCT 20928” and “PRODUCT 20290”, it is found that the salespromotion is to be enhanced by placing these products on adjacentshelves. In this case, the analyzing unit 135 obtains updated contentsof arranging the product with the product ID “PRODUCT 20290” on theshelf B and arranging the product with the product ID “PRODUCT 20660” onthe shelf C. As a result, it is possible to update the store layoutinformation illustrated in FIG. 15 with that as illustrated in FIG. 16.

The output unit 36 performs at least one of first output of outputtinginformation based on the product priority calculated by the analyzingunit 135 to at least one of the recognizing unit 11 and the combiningunit 21 and second output of outputting the updated contents obtained bythe analyzing unit 135 to the managing unit 141.

Since the first output is the same as in the first embodiment, thedescription thereof will not be repeated.

As for the second output, information output by the output unit 36 inthis manner is used for update of sales promotion information at themanaging unit 141, and sales promotion information with higherprobability of motivating the user to buy a product will thus be managedat the third terminal 140.

FIG. 17 is a flowchart illustrating an example of a flow of proceduresof processing performed by the server 130 according to the secondembodiment.

First, the first acquiring unit 31 acquires recognition informationincluding at least a product ID of a product estimated by therecognizing unit 11 from the recognizing unit 11 (the output unit 15),and stores the acquired recognition information in the recognitioninformation storage unit 32 (step S401).

Subsequently, the second acquiring unit 33 acquires combinationinformation including at least a product ID of a product in an image forcombination combined with an image to be combined from the combiningunit 21 (the output unit 25), and stores the acquired combinationinformation in the combination information storage unit 34 (step S403).

Subsequently, the third acquiring unit 133 acquires purchase informationand sales promotion information including at least a product ID of aproduct to be purchased from the managing unit 141 (the output unit144), and stores the acquired purchase information and sales promotioninformation in the sales information storage unit 139 (step S405).

Subsequently, the analyzing unit 135 analyzes a plurality of pieces ofrecognition information stored in the recognition information storageunit 32 to calculate the first product priority of each product,analyzes a plurality of pieces of combination information stored in thecombination information storage unit 34 to calculate the second productpriority of each product, analyzes a plurality of pieces of purchaseinformation stored in the sales information storage unit 139 tocalculate the third product priority of each product, and calculates theproduct priority of each product on the basis of the first productpriority, the second product priority and the third product priority ofeach product (step S407).

Subsequently, the analyzing unit 135 further analyzes whether or notcombination information including a product ID of a product whoseproduct priority satisfies the first predetermined condition exists inthe pieces of combination information, and generates firstrecommendation information recommending related information according tothe analysis result among a plurality of kinds of related information(step S409).

Subsequently, the output unit 36 outputs information based on theproduct priority calculated by the analyzing unit 135 and the firstrecommendation information generated by the analyzing unit 135 to therecognizing unit 11 (step 3411).

Subsequently, if there exists a plurality of categories of a productwhose product priority satisfies the second predetermined condition, theanalyzing unit 135 analyzes the number of occurrences of each of thecategories in the pieces of combination information and generates secondrecommendation information recommending a category with the largestnumber of occurrences (step S413).

Subsequently, the output unit 36 outputs information based on theproduct priority calculated by the analyzing unit 135 and the secondrecommendation information generated by the analyzing unit 135 to thecombining unit 21 (step S415).

Subsequently, the analyzing unit 135 analyzes at least either of aplurality of pieces of recognition information or a plurality of piecesof combination information in addition to a plurality of pieces ofpurchase information to obtain updated contents of the sales promotioninformation (step S417).

Subsequently, the output unit 36 outputs the updated contents of thesales promotion information obtained by the analyzing unit 135 to themanaging unit 141 (step S419).

As described above, according to the second embodiment, products ofgreater interest to the user can be extracted more effectively byfurther analyzing the purchase information to calculate the productpriority. In addition, according to the second embodiment, sinceinformation based on the calculated product priority is output to therecognizing unit and the combining unit, the recognizing unit and thecombining unit can more preferentially present products of greaterinterest to the user by using the information and it is thus possible tofurther increase the probability of motivating the user to buy aproduct.

Furthermore, according to the second embodiment, more effective salesmanagement can be realized by analyzing at least one of the history ofthe recognition information and the history of the combinationinformation in addition to the history of the purchase information,which can lead to analysis and improvement of advertising effectiveness,improvement in product lineup, efficiency in product recommendation tocustomers (improvement in methods for training store staff), improvementin procurement plan, and improvement in store layouts.

Modifications

While examples in which histories of the image recognition terminal thatimplements “focus”, the image combining terminal that implements “try”and the management terminal that manages sales information are used havebeen described in the embodiments described above, the embodiments arenot limited thereto, and histories of terminals using various O2Orelated technologies can be used such as the history of a terminalimplementing “search” that is searching for related product informationaccording to attributes of a product over which “focus” is performed.

Furthermore, in the embodiments described above, the analyzing unit 35need not necessarily analyze ail the histories. That is, the analyzingunit 35 may set any of the weights to 0.

Furthermore, while examples in which the first terminal 10 including therecognizing unit 11 and the second terminal 20 including the combiningunit 21 are different terminals have been described in the embodimentsdescribed above, the recognizing unit 11 and the combining unit 21 maybe included in one terminal 250 as in a system 201 illustrated in FIG.18.

Hardware Configuration

FIG. 19 is a diagram illustrating an example of a hardware configurationof the server according to the embodiments and modifications. The serveraccording to the embodiments and modifications described above includesa control device 901 such as a CPU, a storage device 902 such as a ROMand a RAM, an external storage device 903 such as a HDD, a displaydevice 904 such as a display, an input device 905 such as a keyboard anda mouse, and a communication device 906 such as a communicationinterface (I/F), which is a hardware configuration utilizing a commoncomputer system.

Programs to be executed by the server according to the embodiments andmodifications described above are recorded on a computer readablerecording medium such as a CD-ROM, a CD-R, a memory card, a digitalversatile disk (DVD) and a flexible disk (FD) in a form of a file thatcan be installed or executed, and provided therefrom as a computerprogram product.

Alternatively, the programs to be executed by the server according tothe embodiments and modifications may be stored on a computer systemconnected to a network such as the Internet, and provided by beingdownloaded via the network. Still alternatively, the programs to beexecuted by the server according to the embodiments and modificationsmay be provided or distributed through a network such as the Internet.Still alternatively, the programs to be executed by the server accordingto the embodiments and modifications may be embedded in a ROM or thelike in advance and provided therefrom.

The programs to be executed by the server according to the embodimentsand modifications have modular structures for implementing the unitsdescribed above on a computer system. In an actual hardwareconfiguration, the CPU reads programs from the HDD and executes theprograms on the RAM, whereby the respective units described above areimplemented on a computer system.

For example, the order in which the steps in the flowcharts in theembodiments described above are performed may be changed, a plurality ofsteps may be performed at the same time or the order in which the stepsare performed may be changed each time the steps are performed to theextent that the changes are not inconsistent with the nature thereof.

As described above, according to the embodiments and modificationsdescribed above, information with high probability of motivating theuser to buy a product can be extracted.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. A system for virtual fitting comprising: a firsthandheld terminal comprising: a first processor, a first imager coupledto the first processor, a first display device coupled to the firstprocessor; and a first output card; a second terminal installed in astore comprising: a second processor, a second imager coupled to thesecond processor, and a second display device coupled to the secondprocessor, and a server for providing virtual fitting informationcomprising: a third processor; a recognition information storageconfigured to store a plurality of pieces of the recognition informationoutput from the first terminal, and a combination information storageconfigured to store a plurality of pieces of the combination informationoutput from the second terminal; wherein the first processor isconfigured to: capture an image using the first imager; recognize afirst product image from the captured image by identifying at least oneof a product identification or a plurality of attributes from thecaptured image; estimate a product included in the first product imageand acquire, from the server, first product information includingproduct identification information identifying the product estimatedfrom the first product image and including a plurality of kinds ofrelated information relating to the product estimated from the firstproduct image, at least one of the plurality of kinds of relatedinformation being store information of the store in which the productestimated from the first product image is sold and the second terminalis installed; and per recognizing, output to the server recognitioninformation including the product identification information of theestimated product estimated from the first product image and a date andtime when the recognition information is generated; wherein the secondprocessor is configured to: acquire, from the server, second productinformation of each of a plurality of products, the second productinformation including product identification information identifyingeach of the plurality of products and second product images for each ofthe plurality of products; receive from the second display device asecond product image selected by the user, the second product imagebeing included in the second product information; capture an objectimage that images the user using the second imager; combine, among aplurality of pieces of second product information, the second productimage with the object image; and per combining, output to the servercombination information including the product identification informationincluded in the second product information used for the combining and adate and time when the combination information is generated; wherein thethird processor is configured to: calculate product priorities for theplurality of products based on a first product priority and a secondproduct priority of each product, the first product priority beingcalculated per product by analyzing the stored pieces of recognitioninformation, the second product priority being calculated per product byanalyzing the stored pieces of combination information; set a highervalue in calculating the first product priority of a product representedby product identification information associated with recognitioninformation generated at a date and time closer to a current date andtime; set a higher value in calculating the second product priority of aproduct represented by product identification information associatedwith combination information generated at a date and time closer to acurrent date and time; generate recommendation information recommendingthe store information when a product priority of the product estimatedby the first terminal satisfies a first predetermined condition and theproduct identification information of the product estimated by the firstterminal is not included in the pieces of combination information storedin the combination information storage; and output the recommendationinformation to the first terminal; and wherein, on the basis of therecommendation information output from the server, the first processoris configured to: select the store information from among the pluralityof kinds of related information; and display the store information usingthe first display device.
 2. The system according to claim 1, whereinthe third processor is further configured to: set a higher value to thefirst product priority of a product represented by the productidentification information when the number of the pieces of productidentification information having a same value in the recognitioninformation storage is larger; and set a higher value to the secondproduct priority of a product represented by the product identificationinformation when the number of the pieces of product identificationinformation having in the same combination information storage islarger.
 3. A system for virtual fitting comprising: a first handheldterminal comprising: a first processor, a first imager coupled to thefirst processor, a first display device coupled to the first processor;and a first output card; a second terminal installed in a storecomprising: a second processor; a second imager coupled to the secondprocessor; and a second display device coupled to the second processor;and a server for providing virtual fitting information comprising: athird processor; a recognition information storage configured to store aplurality of pieces of the recognition information output from the firstterminal; and a combination information storage configured to store aplurality of pieces of the combination information output from thesecond terminal; wherein the first processor is configured to: capturean image using the first imager; recognize a first product image fromthe captured image by identifying at least one of a productidentification or a plurality of attributes from the captured image;estimate a product included in the first product image and acquire, fromthe server, first product information including product identificationinformation identifying the product estimated from the first productimage and including a plurality of kinds of related information relatingto the product estimated from the first product image, at least one ofthe plurality of kinds of related information being information of thestore in which the product estimated from the first product image issold and the second terminal is installed; and per recognizing, outputto the server recognition information including the productidentification information of the estimated product, estimated from thefirst product image and a date and time when the recognition informationis generated; wherein the second processor is configured to: acquire,from the server, second product information of each of a plurality ofproducts, the second product information including productidentification information identifying each of the plurality of productsand second product images for each of the plurality of products; receivefrom the second display device a second product image selected by theuser, the second product image being included in the second productinformation; capture an object image that images the user using thesecond imager; combine, among a plurality of pieces of second productinformation, the second product image with the object image; and percombining, output to the server combination information including theproduct identification information included in the second productinformation used for the combining and a date and time when thecombination information is generated; wherein the third processor isconfigured to: acquire sales promotion information and purchaseinformation associated with the plurality of products, the purchaseinformation including product identification information and a date andtime when the purchase information is generated; store the salespromotion information and purchase information in the sales informationstorage; calculate product priorities for the plurality of productsbased on a first product priority, a second product priority, a thirdproduct priority of each product, the first product priority beingcalculated per product by analyzing the stored pieces of recognitioninformation, the second product priority being calculated per product byanalyzing the stored pieces of combination information, and the thirdproduct priority being calculated per product by analyzing the storedpieces of purchase information; set a higher value in calculating thefirst product priority of a product represented by productidentification information associated with recognition informationgenerated at a date and time closer to a current date and time; set ahigher value in calculating the second product priority of a productrepresented by product identification information associated withcombination information generated at a date and time closer to a currentdate and time; set a higher value in calculating the third productpriority of a product represented by product identification informationassociated with purchase information generated at a date and time closerto a current date and time; generate recommendation informationrecommending the store information and obtain information for updatingthe stored sales promotion information by analyzing informationincluding the stored purchase information when a product priority of theproduct estimated by the first terminal satisfies a first predeterminedcondition and the product identification information of the productestimated by the first terminal is not included in the pieces ofcombination information stored in the combination information storage;and output the recommendation information to the first terminal and theinformation for updating the stored sales promotion information to thethird terminal, wherein on the basis of the recommendation informationoutput from the server, the processing circuitry of the first terminalselects the store information from among the plurality of kinds ofrelated information and displays the store information.
 4. The systemaccording to claim 3, wherein the third processor is further configuredto: set a higher value to the first product priority of a productrepresented by the product identification information when the number ofthe pieces of product identification information having a same value inthe recognition information storage is larger; set a higher value to thesecond product priority of a product represented by the productidentification information as the number of the pieces of productidentification information having a same combination information storageis larger; and set a higher value to the third product priority of aproduct represented by the product identification information as thenumber of the pieces of product identification information having a samein the sales information storage is larger.
 5. The system according toclaim 3, wherein the acquired recognition information further includesinformation associated with the product images; the acquired salespromotion information further includes information associated with asales promotion that includes at least one of the product images; andthe sales promotion information is updated when a number of purchases ofa product corresponding to the at least one of the product imagessatisfies a predetermined condition, the updated information obtained byanalyzing the purchase information and a number of product imagesassociated with the product included in the stored recognitioninformation.
 6. The system according to claim 3, wherein: the acquiredcombination information further includes information associated with aproduct image and an object image included in a combination; theacquired sales promotion information further includes informationassociated with a sales promotion that includes the combination; and thesales promotion information is updated when a product corresponding tothe product image included in the combination is associated with anumber of purchases that satisfies a predetermined condition, theupdated information being obtained by analyzing the purchase informationand combination information associated with the combination.
 7. A methodfor a virtual fitting system comprising a first handheld terminal, asecond terminal installed in a store, and a server for providing virtualfitting information, the method comprising: by the first terminal:capturing an image using a first imager; recognizing a first productimage from the captured image by identifying at least one of a productidentification or a plurality of attributes from the captured image;estimating a product included in the first product image; acquiring,from the server, first product information including productidentification information identifying the product estimated from thefirst product image and including a plurality of kinds of relatedinformation relating associated with the product estimated from thefirst product image, at least one of the plurality of kinds of relatedinformation being store information of the store in which the productestimated from the first product image is sold and the second terminalis installed; and per recognizing, outputting to the server, recognitioninformation including the product identification information of theestimated product, estimated from the first product image and a date andtime when the recognition information is generated; by the secondterminal: acquiring, from the server, second product information of eachof a plurality of products, the second product information includingproduct identification information identifying each of the plurality ofproducts and second product images for each of the plurality ofproducts; receiving from a display device a second product imageselected by the user, the second product image being included in thesecond product information; capturing an object image that images theuser using a second imager; combining, among a plurality of pieces ofsecond product information, the second product image with the objectimage; and per combining, outputting to the server combinationinformation including the product identification information included inthe second product information used for the combining and a date andtime when the combination information is generated; and by the server:storing a plurality of pieces of the recognition information output fromthe first terminal in a recognition information storage; storing aplurality of pieces of the combination information output from thesecond terminal in a combination information storage; calculatingproduct priorities for the plurality of products based on a firstproduct priority and a second product priority of each product, thefirst product priority being calculated per product by analyzing thestored pieces of recognition information, the second product prioritybeing calculated per product by analyzing the stored pieces ofcombination information; setting a higher value in calculating the firstproduct priority of a product represented by product identificationinformation associated with recognition information generated at a dateand time closer to a current date and time; setting a higher value incalculating the second product priority of a product represented byproduct identification information associated with combinationinformation generated at a date and time closer to a current date andtime; generating recommendation information recommending the storeinformation when a product priority of the product estimated by thefirst terminal satisfies a first predetermined condition and the productidentification information of the product estimated by the firstterminal is not included in the pieces of combination information storedin the combination information storage; and outputting, based therecommendation information to the first terminal; and by the firstterminal on the basis of the recommendation information output from theserver, selecting the store information from among the plurality ofkinds of related information and displaying the store information. 8.The method of claim 7, wherein the method further comprises: by theserver: setting a higher value to the first product priority of aproduct represented by the product identification information when thenumber of the pieces of product identification information having a samevalue in the recognition information storage is larger; and setting ahigher value to the second product priority of a product represented bythe product identification information when the number of the pieces ofproduct identification information having in the same combinationinformation storage is larger.
 9. The system of claim 1, wherein thefirst imager comprises a first digital camera; the first output cardcomprises a network interface card (NIC); and the first display devicecomprises at least one of a liquid crystal display or a touch paneldisplay.
 10. The system of claim 9, wherein the second imager comprisesa second digital camera; and the second display device comprises atleast one of a liquid crystal display or a touch panel display.
 11. Thesystem of claim 10, wherein the first display device comprises a liquidcrystal display; and the second display comprises a touch panel.
 12. Thesystem of claim 1, wherein the plurality of attributes comprise brand,color, and material; and the captured image comprises an advertisingimage of the estimated product.
 13. The system of claim 1, wherein therecognition information storage comprises at least one of a hard diskdrive, a solid state drive, a memory card, an optical disk, or a randomaccess memory; and the combination information storage comprises atleast one of a hard disk drive, a solid state drive, a memory card, anoptical disk, or a random access memory.
 14. The system of claim 13,wherein the recognition information storage comprises a solid statedrive; and the combination information storage comprises a hard diskdrive.
 15. The system of claim 1, wherein the first output card iscoupled to the first display device and the first processor.
 16. Thesystem of claim 15, wherein the second terminal further comprises anetwork interface card (NIC) coupled to the second processor and thesecond display device.
 17. The system of claim 1, wherein the firstterminal further comprises at least one hard drive coupled to the firstprocessor and storing feedback information; and the second terminalfurther comprises at least one hard drive coupled to the secondprocessor and storing feedback information.
 18. The system of claim 1,wherein the third processor is coupled in parallel with the recognitioninformation storage and the combination information storage.
 19. Thesystem of claim 18, wherein the server further comprises: a hard drivestoring the second product information; and a network interface card(NIC) coupled to the third processor and the hard drive.
 20. The systemof claim 19, wherein the hard drive stores the second productinformation in a data structure comprising product identification,attribute information, accompanying information, and a group of images.